Browsing by Author "Taştan, Ö."

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Open Access
MatchMaker: A deep learning framework for drug synergy prediction
(IEEE, 2021-06-04) Kuru, Halil İbrahim; Taştan, Ö.; Çiçek, Ercüment
Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to 15% correlation and 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker
Open Access
Revisiting the complex architecture of ALS in Turkey: expanding genotypes, shared phenotypes, molecular networks, and a public variant database
(John Wiley and Sons, 2020) Tunca, C.; Şeker, T.; Akçimen, F.; Coşkun, C.; Bayraktar, E.; Palvadeau, R.; Zor, S.; Koçoğlu, C.; Kartal, E.; Şen, N. E.; Hamzeiy, H.; Özoğuz-Erimiş, A.; Norman, Utku; Karakahya, Oğuzhan; Olgun, Gülden; Akgün, T.; Durmuş, H.; Şahin, E.; Çakar, A.; Başar-Gürsoy, E.; Babacan-Yıldız, G.; İşak, B.; Uluç, K.; Hanağası, H.; Bilgiç, B.; Turgut, N.; Aysal, F.; Ertaş, M.; Boz, C.; Kotan, D.; İdrisoğlu, H.; Soysal, A.; Uzun-Adatepe, N.; Akalın, M. A.; Koç, F.; Tan, E.; Oflazer, P.; Deymeer, F.; Taştan, Ö.; Çiçek, A. Ercüment; Kavak, E.; Parman, Y.; Başak, A. N.
The last decade has proven that amyotrophic lateral sclerosis (ALS) is clinically and genetically heterogeneous, and that the genetic component in sporadic cases might be stronger than expected. This study investigates 1,200 patients to revisit ALS in the ethnically heterogeneous yet inbred Turkish population. Familial ALS (fALS) accounts for 20% of our cases. The rates of consanguinity are 30% in fALS and 23% in sporadic ALS (sALS). Major ALS genes explained the disease cause in only 35% of fALS, as compared with ~70% in Europe and North America. Whole exome sequencing resulted in a discovery rate of 42% (53/127). Whole genome analyses in 623 sALS cases and 142 population controls, sequenced within Project MinE, revealed well‐established fALS gene variants, solidifying the concept of incomplete penetrance in ALS. Genome‐wide association studies (GWAS) with whole genome sequencing data did not indicate a new risk locus. Coupling GWAS with a coexpression network of disease‐associated candidates, points to a significant enrichment for cell cycle‐ and division‐related genes. Within this network, literature text‐mining highlights DECR1, ATL1, HDAC2, GEMIN4, and HNRNPA3 as important genes. Finally, information on ALS‐related gene variants in the Turkish cohort sequenced within Project MinE was compiled in the GeNDAL variant browser (www.gendal.org).
Open Access
SPADIS: An algorithm for selecting predictive and diverse SNPs in GWAS
(IEEE, 2021) Yılmaz, Serhan; Taştan, Ö.; Çiçek, A. Ercüment
Phenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants identified in genome-wide association studies (GWAS). Many methods have been developed to select a subset of variant loci, which are associated with or predictive of the phenotype. Selecting connected SNPs on SNP-SNP networks have been proven successful in finding biologically interpretable and predictive SNPs. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. In this paper, we propose a novel method called SPADIS that favors the selection of remotely located SNPs in order to account for their complementary effects in explaining a phenotype. SPADIS selects a diverse set of loci on a SNP-SNP network. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor approximation to the optimal solution. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana with continuous flowering time phenotypes. SPADIS has better average phenotype prediction performance in 15 out of 17 phenotypes when the same number of SNPs are selected and provides consistent improvements across multiple networks and settings on average. Moreover, it identifies more candidate genes and runs faster.
Open Access
Uncovering complementary sets of variants for predicting quantitative phenotypes
(Oxford University Press, 2021-12-02) Yılmaz, S.; Fakhouri, Mohamad; Koyutürk, M.; Çiçek, A. E.; Taştan, Ö.
Motivation: Genome-wide association studies show that variants in individual genomic loci alone are not sufficient to explain the heritability of complex, quantitative phenotypes. Many computational methods have been developed to address this issue by considering subsets of loci that can collectively predict the phenotype. This problem can be considered a challenging instance of feature selection in which the number of dimensions (loci that are screened) is much larger than the number of samples. While currently available methods can achieve decent phenotype prediction performance, they either do not scale to large datasets or have parameters that require extensive tuning. Results: We propose a fast and simple algorithm, Macarons, to select a small, complementary subset of variants by avoiding redundant pairs that are likely to be in linkage disequilibrium. Our method features two interpretable parameters that control the time/performance trade-off without requiring parameter tuning. In our computational experiments, we show that Macarons consistently achieves similar or better prediction performance than state-ofthe-art selection methods while having a simpler premise and being at least two orders of magnitude faster. Overall, Macarons can seamlessly scale to the human genome with 107 variants in a matter of minutes while taking the dependencies between the variants into account. Availabilityand implementation: Macarons is available in Matlab and Python at https://github.com/serhan-yilmaz/macarons.